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<div class="title">svm_wrapper.h</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160; <span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">  * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">  *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">  *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment">  *  Copyright (c) 2010-2012, Willow Garage, Inc.</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment">  *  Copyright (c) 2000-2012 Chih-Chung Chang and Chih-Jen Lin</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment">  *</span></div>
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<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment">  *  Redistribution and use in source and binary forms, with or without</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment">  *  modification, are permitted provided that the following conditions</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment">  *  are met:</span></div>
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<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment">  *   * Redistributions of source code must retain the above copyright</span></div>
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<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160; </div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="preprocessor">#ifndef PCL_SVM_WRAPPER_H_</span></div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="preprocessor">#define PCL_SVM_WRAPPER_H_</span></div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160; </div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#include &lt;stdio.h&gt;</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">#include &lt;stdlib.h&gt;</span></div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="preprocessor">#include &lt;string.h&gt;</span></div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">#include &lt;ctype.h&gt;</span></div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">#include &lt;errno.h&gt;</span></div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="preprocessor">#include &lt;iostream&gt;</span></div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="preprocessor">#include &lt;fstream&gt;</span></div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="preprocessor">#include &lt;pcl/common/eigen.h&gt;</span></div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="preprocessor">#include &lt;vector&gt;</span></div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160; </div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="preprocessor">#include &lt;pcl/console/time.h&gt;</span></div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160; </div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;<span class="preprocessor">#include &lt;pcl/ml/svm.h&gt;</span></div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<span class="preprocessor">#define Malloc(type,n) static_cast&lt;type *&gt; (malloc((n)*sizeof(type)))</span></div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160; </div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;<span class="keyword">namespace </span>pcl</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;{</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160; </div>
<div class="line"><a name="l00066"></a><span class="lineno"><a class="line" href="structpcl_1_1_s_v_m_param.html">   66</a></span>&#160;  <span class="keyword">struct </span><a class="code" href="structpcl_1_1_s_v_m_param.html">SVMParam</a>: <a class="code" href="structsvm__parameter.html">svm_parameter</a></div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;  {</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    <a class="code" href="structpcl_1_1_s_v_m_param.html">SVMParam</a> ()</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    {</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;      svm_type = C_SVC; <span class="comment">// C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR</span></div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;      kernel_type = RBF; <span class="comment">// LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED</span></div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;      degree = 3; <span class="comment">// for poly</span></div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;      gamma = 0; <span class="comment">// 1/num_features {for poly/rbf/sigmoid}</span></div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;      coef0 = 0; <span class="comment">//  for poly/sigmoid</span></div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160; </div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;      nu = 0.5; <span class="comment">// for NU_SVC, ONE_CLASS, and NU_SVR</span></div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;      cache_size = 100; <span class="comment">// in MB</span></div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;      C = 1; <span class="comment">// for C_SVC, EPSILON_SVR and NU_SVR</span></div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;      eps = 1e-3; <span class="comment">// stopping criteria</span></div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;      p = 0.1; <span class="comment">// for EPSILON_SVR</span></div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;      shrinking = 0; <span class="comment">// use the shrinking heuristics</span></div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;      probability = 0; <span class="comment">// do probability estimates</span></div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160; </div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;      nr_weight = 0; <span class="comment">// for C_SVC</span></div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;      weight_label = NULL; <span class="comment">// for C_SVC</span></div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;      weight = NULL; <span class="comment">// for C_SVC</span></div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    }</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  };</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160; </div>
<div class="line"><a name="l00092"></a><span class="lineno"><a class="line" href="structpcl_1_1_s_v_m_model.html">   92</a></span>&#160;  <span class="keyword">struct </span><a class="code" href="structpcl_1_1_s_v_m_model.html">SVMModel</a>: <a class="code" href="structsvm__model.html">svm_model</a></div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;  {</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    <a class="code" href="structpcl_1_1_s_v_m_model.html">SVMModel</a> ()</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    {</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;      l = 0;</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;      probA = NULL;</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;      probB = NULL;</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    }</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;  };</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160; </div>
<div class="line"><a name="l00104"></a><span class="lineno"><a class="line" href="structpcl_1_1_s_v_m_data_point.html">  104</a></span>&#160;  <span class="keyword">struct </span><a class="code" href="structpcl_1_1_s_v_m_data_point.html">SVMDataPoint</a></div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;  {</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    <span class="keywordtype">int</span> idx; <span class="comment">// It&#39;s the feature index. It has to be an integer number greater or equal to zero.</span></div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    <span class="keywordtype">float</span> value; <span class="comment">// The value assigned to the correspondent feature.</span></div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160; </div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    <a class="code" href="structpcl_1_1_s_v_m_data_point.html">SVMDataPoint</a> () : idx (-1), value (0)</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    {</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    }</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;  };</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160; </div>
<div class="line"><a name="l00117"></a><span class="lineno"><a class="line" href="structpcl_1_1_s_v_m_data.html">  117</a></span>&#160;  <span class="keyword">struct </span><a class="code" href="structpcl_1_1_s_v_m_data.html">SVMData</a></div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;  {</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <span class="keywordtype">double</span> label; <span class="comment">// Pointer to the label value. It is a mandatory to train the classifier.</span></div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    std::vector&lt;pcl::SVMDataPoint&gt; SV; <span class="comment">// Vector of features for the specific sample.</span></div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160; </div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    <a class="code" href="structpcl_1_1_s_v_m_data.html">SVMData</a> () : label (std::numeric_limits&lt;double&gt;::signaling_NaN())</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    {</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    }</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;  };</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160; </div>
<div class="line"><a name="l00129"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html">  129</a></span>&#160;  <span class="keyword">class </span><a class="code" href="classpcl_1_1_s_v_m.html">SVM</a></div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;  {</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    <span class="keyword">protected</span>:</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;      std::vector&lt;SVMData&gt; training_set_; <span class="comment">// Basic training set</span></div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;      <a class="code" href="structsvm__problem.html">svm_problem</a> prob_; <span class="comment">// contains the problem (vector of samples with their features)</span></div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;      <a class="code" href="structpcl_1_1_s_v_m_model.html">SVMModel</a> model_; <span class="comment">// model of the classifier</span></div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;      <a class="code" href="structsvm__scaling.html">svm_scaling</a> scaling_; <span class="comment">// for the best model training, the input dataset is scaled and the scaling factors are stored here</span></div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;      <a class="code" href="structpcl_1_1_s_v_m_param.html">SVMParam</a> param_; <span class="comment">// it stores the training parameters</span></div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;      std::string class_name_; <span class="comment">// The SVM class name.</span></div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160; </div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;      <span class="keywordtype">char</span> *line_; <span class="comment">// buffer for line reading</span></div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;      <span class="keywordtype">int</span> max_line_len_; <span class="comment">// max line length in the input file</span></div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;      <span class="keywordtype">bool</span> labelled_training_set_; <span class="comment">// it stores whether the input set of samples is labelled</span></div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;<span class="comment"></span>      <span class="keyword">static</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00144"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#af8b6779e2fbd5a636d79c5a8c77cf102">  144</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m.html#af8b6779e2fbd5a636d79c5a8c77cf102">printNull</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *) {}; </div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;      </div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;      <span class="keywordtype">char</span>* </div>
<div class="line"><a name="l00148"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#a25c695eb4d94ac665b2ecccb4497aa85">  148</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m.html#a25c695eb4d94ac665b2ecccb4497aa85">readline</a> (FILE *input); </div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160; </div>
<div class="line"><a name="l00151"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#ae13d89577f55fcff9a570ccf77ee9e37">  151</a></span>&#160;      <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1_s_v_m.html#ae13d89577f55fcff9a570ccf77ee9e37">exitInputError</a> (<span class="keywordtype">int</span> line_num)</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;      {</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;        fprintf (stderr, <span class="stringliteral">&quot;Wrong input format at line %d\n&quot;</span>, line_num);</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;        exit (1);</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;      }</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;      </div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;      <span class="keyword">inline</span> <span class="keyword">const</span> std::string&amp;</div>
<div class="line"><a name="l00159"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#a42bf58b7fe08d473a2f4a595ded0fb4b">  159</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m.html#a42bf58b7fe08d473a2f4a595ded0fb4b">getClassName</a> ()<span class="keyword"> const</span></div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;<span class="keyword">      </span>{</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;        <span class="keywordflow">return</span> (class_name_);</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;      }</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;      </div>
<div class="line"><a name="l00165"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#ab9110ca99ee2fa34573133fe4a083fe2">  165</a></span>&#160;      <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1_s_v_m.html#ab9110ca99ee2fa34573133fe4a083fe2">adaptInputToLibSVM</a> (std::vector&lt;SVMData&gt; training_set, <a class="code" href="structsvm__problem.html">svm_problem</a> &amp;prob);</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160; </div>
<div class="line"><a name="l00168"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#ad8e2c4441d6288270bdf7c36f4c4bbb2">  168</a></span>&#160;      <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1_s_v_m.html#ad8e2c4441d6288270bdf7c36f4c4bbb2">adaptLibSVMToInput</a> (std::vector&lt;SVMData&gt; &amp;training_set, <a class="code" href="structsvm__problem.html">svm_problem</a> prob);</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160; </div>
<div class="line"><a name="l00171"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#a0ec65c97223faf337970f834f304746f">  171</a></span>&#160;      <span class="keywordtype">bool</span> <a class="code" href="classpcl_1_1_s_v_m.html#a0ec65c97223faf337970f834f304746f">loadProblem</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename, <a class="code" href="structsvm__problem.html">svm_problem</a> &amp;prob);</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160; </div>
<div class="line"><a name="l00174"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#ac256c5efa13d6759dfc6e77e17eae4e6">  174</a></span>&#160;      <span class="keywordtype">bool</span> <a class="code" href="classpcl_1_1_s_v_m.html#ac256c5efa13d6759dfc6e77e17eae4e6">saveProblem</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename, <span class="keywordtype">bool</span> labelled);</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160; </div>
<div class="line"><a name="l00177"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#ae604dbc0d535f8b0064b95cac1a15895">  177</a></span>&#160;      <span class="keywordtype">bool</span> <a class="code" href="classpcl_1_1_s_v_m.html#ae604dbc0d535f8b0064b95cac1a15895">saveProblemNorm</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename, <a class="code" href="structsvm__problem.html">svm_problem</a> prob_, <span class="keywordtype">bool</span> labelled);</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160; </div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    <span class="keyword">public</span>:</div>
<div class="line"><a name="l00181"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#acf652ea11ea0c106b1cbbce00554185c">  181</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m.html#acf652ea11ea0c106b1cbbce00554185c">SVM</a> () : </div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;        training_set_ (), prob_ (), model_ (), scaling_ (), param_ (), </div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;        class_name_ (), line_ (NULL), max_line_len_ (10000), labelled_training_set_ (1)</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;      {</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;      }</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160; </div>
<div class="line"><a name="l00188"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#aa1bb58c6d4ea4cc0bef4343371da4307">  188</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m.html#aa1bb58c6d4ea4cc0bef4343371da4307">~SVM</a> ()</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;      {</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;        svm_destroy_param (&amp;param_); <span class="comment">// delete parameters</span></div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160; </div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;        <span class="keywordflow">if</span> (scaling_.max &gt; 0)</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;          free (scaling_.obj); <span class="comment">// delete scaling factors</span></div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160; </div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;        <span class="comment">// delete the problem</span></div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;        <span class="keywordflow">if</span> (prob_.l &gt; 0)</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;        {</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;          free (prob_.x);</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;          free (prob_.y);</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;        }</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;      }</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160; </div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00205"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#acc4bad66d55915dd09977192ab8311b5">  205</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m.html#acc4bad66d55915dd09977192ab8311b5">getLabel</a> (std::vector&lt;int&gt; &amp;labels)</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;      {</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;        <span class="keywordtype">int</span> nr_class = svm_get_nr_class (&amp;model_);</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        <span class="keywordtype">int</span> *labels_ = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span> *<span class="keyword">&gt;</span> (malloc (nr_class * <span class="keyword">sizeof</span> (<span class="keywordtype">int</span>)));</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        svm_get_labels (&amp;model_, labels_);</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160; </div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0 ; j &lt; nr_class; j++)</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;          labels.push_back (labels_[j]);</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160; </div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;        free (labels_);</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;      };</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160; </div>
<div class="line"><a name="l00218"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m.html#a3f59b5f036691d050e7d1b8845083732">  218</a></span>&#160;      <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1_s_v_m.html#a3f59b5f036691d050e7d1b8845083732">saveClassifierModel</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename)</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;      {</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;        <span class="comment">// exit if model has no data</span></div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;        <span class="keywordflow">if</span> (model_.l == 0)</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;          <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160; </div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        <span class="keywordflow">if</span> (svm_save_model (filename, &amp;model_))</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;        {</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;          fprintf (stderr, <span class="stringliteral">&quot;can&#39;t save model to file %s\n&quot;</span>, filename);</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;          exit (1);</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;        }</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;      };</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;  };</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160; </div>
<div class="line"><a name="l00236"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html">  236</a></span>&#160;  <span class="keyword">class </span><a class="code" href="classpcl_1_1_s_v_m_train.html">SVMTrain</a> : <span class="keyword">public</span> <a class="code" href="classpcl_1_1_s_v_m.html">SVM</a></div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;  {</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    <span class="keyword">protected</span>:</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;      <span class="keyword">using</span> SVM::labelled_training_set_;</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;      <span class="keyword">using</span> SVM::model_;</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;      <span class="keyword">using</span> SVM::line_;</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;      <span class="keyword">using</span> SVM::max_line_len_;</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;      <span class="keyword">using</span> SVM::training_set_;</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;      <span class="keyword">using</span> SVM::prob_;</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;      <span class="keyword">using</span> SVM::scaling_;</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;      <span class="keyword">using</span> SVM::param_;</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;      <span class="keyword">using</span> SVM::class_name_;</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160; </div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;      <span class="keywordtype">bool</span> debug_; <span class="comment">// Set to 1 to see the training output</span></div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;      <span class="keywordtype">int</span> cross_validation_; <span class="comment">// Set too 1 for cross validating the classifier</span></div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;      <span class="keywordtype">int</span> nr_fold_; <span class="comment">// Number of folds to be used during cross validation. It indicates in how many parts is split the input training set.</span></div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160; </div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;      <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00255"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#a187c6e44967d05ad37ecee814ff04a41">  255</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#a187c6e44967d05ad37ecee814ff04a41">doCrossValidation</a>();</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;      </div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;      <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00260"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#ab996f6bdd3ac6565558722f493c75e67">  260</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#ab996f6bdd3ac6565558722f493c75e67">scaleFactors</a> (std::vector&lt;SVMData&gt; training_set, <a class="code" href="structsvm__scaling.html">svm_scaling</a> &amp;scaling);</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;      </div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    <span class="keyword">public</span>:</div>
<div class="line"><a name="l00264"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#af958b338c84171fbc044b271561a66ef">  264</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#af958b338c84171fbc044b271561a66ef">SVMTrain</a>() : debug_ (0), cross_validation_ (0), nr_fold_ (0)</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;      {</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        class_name_ = <span class="stringliteral">&quot;SVMTrain&quot;</span>;</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;        svm_set_print_string_function (&amp;<a class="code" href="classpcl_1_1_s_v_m.html#af8b6779e2fbd5a636d79c5a8c77cf102">printNull</a>); <span class="comment">// Default to NULL to not print debugging info</span></div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;      }</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160; </div>
<div class="line"><a name="l00271"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#a4d3043d078eda40c1ced194ec6ef89df">  271</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#a4d3043d078eda40c1ced194ec6ef89df">~SVMTrain</a> ()</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;      {</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;        <span class="keywordflow">if</span> (model_.l &gt; 0)</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;          svm_free_model_content (&amp;model_);</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;      }</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160; </div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00279"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#ac032e2dc6d89909d72aebc5cb46806f8">  279</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#ac032e2dc6d89909d72aebc5cb46806f8">setParameters</a> (<a class="code" href="structpcl_1_1_s_v_m_param.html">SVMParam</a> param)</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;      {</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;        param_ = param;</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;      }</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160; </div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;      <a class="code" href="structpcl_1_1_s_v_m_param.html">SVMParam</a></div>
<div class="line"><a name="l00286"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#af70b307e8b0d305325380e49349c9158">  286</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#af70b307e8b0d305325380e49349c9158">getParameters</a> ()</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;      {</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;        <span class="keywordflow">return</span> param_;</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;      }</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160; </div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;      <a class="code" href="structpcl_1_1_s_v_m_model.html">SVMModel</a></div>
<div class="line"><a name="l00293"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#a74a008298d1d42571b181c0383416010">  293</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#a74a008298d1d42571b181c0383416010">getClassifierModel</a> ()</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;      {</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;        <span class="keywordflow">return</span> model_;</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;      }</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160; </div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00300"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#a83f2a5e13dd354bd0fdaab63eebc1eea">  300</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#a83f2a5e13dd354bd0fdaab63eebc1eea">setInputTrainingSet</a> (std::vector&lt;SVMData&gt; training_set)</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;      {</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;        training_set_.insert (training_set_.end(), training_set.begin(), training_set.end());</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;      }</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160; </div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;      std::vector&lt;SVMData&gt;</div>
<div class="line"><a name="l00307"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#a9937510055076d410ebb80def036e020">  307</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#a9937510055076d410ebb80def036e020">getInputTrainingSet</a> ()</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;      {</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;        <span class="keywordflow">return</span> training_set_;</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;      }</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160; </div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00314"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#a3656dabad7ef92656ccfd319657ee392">  314</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#a3656dabad7ef92656ccfd319657ee392">resetTrainingSet</a> ()</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;      {</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;        training_set_.clear();</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;      }</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160; </div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;      <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00322"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#ab860aa193b6775d19bc97ec3d8bedf8f">  322</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#ab860aa193b6775d19bc97ec3d8bedf8f">trainClassifier</a> ();</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160; </div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;      <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00327"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#a910cc9a619fd9f4c28e1af99f28aa002">  327</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#a910cc9a619fd9f4c28e1af99f28aa002">loadProblem</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename)</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;      {</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_s_v_m.html#a0ec65c97223faf337970f834f304746f">SVM::loadProblem</a> (filename, prob_);</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;      };</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160; </div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00334"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#ab9959f5e2ee6c39ac14ed8e09b6afc98">  334</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#ab9959f5e2ee6c39ac14ed8e09b6afc98">setDebugMode</a> (<span class="keywordtype">bool</span> in)</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;      {</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;        debug_ = in;</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160; </div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;        <span class="keywordflow">if</span> (in)</div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;          svm_set_print_string_function (NULL);</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;        <span class="keywordflow">else</span></div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;          svm_set_print_string_function (&amp;<a class="code" href="classpcl_1_1_s_v_m.html#af8b6779e2fbd5a636d79c5a8c77cf102">printNull</a>);</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;      };</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160; </div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;      <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00347"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#a9fc90b124c8c72e8c82e9d0657a8d22e">  347</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#a9fc90b124c8c72e8c82e9d0657a8d22e">saveTrainingSet</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename)</div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;      {</div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_s_v_m.html#ac256c5efa13d6759dfc6e77e17eae4e6">SVM::saveProblem</a> (filename, 1);</div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;      };</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160; </div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;      <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00355"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_train.html#a993cd35745435d46b54597846a03005a">  355</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_train.html#a993cd35745435d46b54597846a03005a">saveNormTrainingSet</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename)</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;      {</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_s_v_m.html#ae604dbc0d535f8b0064b95cac1a15895">SVM::saveProblemNorm</a> (filename, prob_, 1);</div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;      };</div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;  };</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160; </div>
<div class="line"><a name="l00364"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html">  364</a></span>&#160;  <span class="keyword">class </span><a class="code" href="classpcl_1_1_s_v_m_classify.html">SVMClassify</a> : <span class="keyword">public</span> <a class="code" href="classpcl_1_1_s_v_m.html">SVM</a></div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;  {</div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    <span class="keyword">protected</span>:</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;      <span class="keyword">using</span> SVM::labelled_training_set_;</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;      <span class="keyword">using</span> SVM::model_;</div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;      <span class="keyword">using</span> SVM::line_;</div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;      <span class="keyword">using</span> SVM::max_line_len_;</div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;      <span class="keyword">using</span> SVM::training_set_;</div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;      <span class="keyword">using</span> SVM::prob_;</div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;      <span class="keyword">using</span> SVM::scaling_;</div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;      <span class="keyword">using</span> SVM::param_;</div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;      <span class="keyword">using</span> SVM::class_name_;</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160; </div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;      <span class="keywordtype">bool</span> model_extern_copied_; <span class="comment">// Set to 0 if the model is loaded from an extern file.</span></div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;      <span class="keywordtype">bool</span> predict_probability_; <span class="comment">// Set to 1 to predict probabilities.</span></div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;      std::vector&lt; std::vector&lt;double&gt; &gt; prediction_; <span class="comment">// It stores the resulting prediction.</span></div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;      </div>
<div class="line"><a name="l00382"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#ae4c3f1f28b86a9f743f23c049f37b75a">  382</a></span>&#160;      <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1_s_v_m_classify.html#ae4c3f1f28b86a9f743f23c049f37b75a">scaleProblem</a> (<a class="code" href="structsvm__problem.html">svm_problem</a> &amp;input, <a class="code" href="structsvm__scaling.html">svm_scaling</a> scaling);</div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;      </div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;    <span class="keyword">public</span>:</div>
<div class="line"><a name="l00386"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a8a44eed8244805fb26112aef65153b5d">  386</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a8a44eed8244805fb26112aef65153b5d">SVMClassify</a> () : model_extern_copied_ (0), predict_probability_ (0)</div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;      {</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;        class_name_ = <span class="stringliteral">&quot;SvmClassify&quot;</span>;</div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;      }</div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160; </div>
<div class="line"><a name="l00392"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a31aa9eab4c87ee424344e66b58f64e3b">  392</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a31aa9eab4c87ee424344e66b58f64e3b">~SVMClassify</a> ()</div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;      {</div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;        <span class="keywordflow">if</span> (!model_extern_copied_ &amp;&amp; model_.l &gt; 0)</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;          svm_free_model_content (&amp;model_);</div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;      }</div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160; </div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00400"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#aa2e72c9b20d5f0b997b2146da40c75ef">  400</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#aa2e72c9b20d5f0b997b2146da40c75ef">setInputTrainingSet</a> (std::vector&lt;SVMData&gt; training_set)</div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;      {</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;        assert (training_set.size() &gt; 0);</div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160; </div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;        <span class="keywordflow">if</span> (scaling_.max == 0)</div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;        {</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;          <span class="comment">// to be sure to have loaded the scaling</span></div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;          PCL_ERROR (<span class="stringliteral">&quot;[pcl::%s::setInputTrainingSet] Classifier model not loaded!\n&quot;</span>, <a class="code" href="classpcl_1_1_s_v_m.html#a42bf58b7fe08d473a2f4a595ded0fb4b">getClassName</a> ().c_str ());</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;          <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;        }</div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;        </div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;        training_set_.insert (training_set_.end(), training_set.begin(), training_set.end());</div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;        <a class="code" href="classpcl_1_1_s_v_m.html#ab9110ca99ee2fa34573133fe4a083fe2">SVM::adaptInputToLibSVM</a> (training_set_, prob_);</div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;      }</div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160; </div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;      std::vector&lt;SVMData&gt;</div>
<div class="line"><a name="l00417"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a31fc76d6055887a06e08d5e6dc23d5ec">  417</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a31fc76d6055887a06e08d5e6dc23d5ec">getInputTrainingSet</a> ()</div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;      {</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;        <span class="keywordflow">return</span> training_set_;</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;      }</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160; </div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00424"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a1f8c738896fa7d85def12eb947891bc4">  424</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a1f8c738896fa7d85def12eb947891bc4">resetTrainingSet</a>()</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;      {</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;        training_set_.clear();</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;      }</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160; </div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;      <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00432"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#aafd5d50bf3c35d31db549b0bff7595c2">  432</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#aafd5d50bf3c35d31db549b0bff7595c2">loadClassifierModel</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename);</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160; </div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00436"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#afc0eb03a8962cb637ac99eaf1e2d1afc">  436</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#afc0eb03a8962cb637ac99eaf1e2d1afc">getClassificationResult</a> (std::vector&lt; std::vector&lt;double&gt; &gt; &amp;out)</div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;      {</div>
<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;        out.clear ();</div>
<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;        out.insert (out.begin(), prediction_.begin(), prediction_.end());</div>
<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;      }</div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160; </div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00444"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a42721792e7fbad28333c9cdbcdfb79b4">  444</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a42721792e7fbad28333c9cdbcdfb79b4">saveClassificationResult</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename);</div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160; </div>
<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00448"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a9e5bb9b2305d89e5006ab729657bd139">  448</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a9e5bb9b2305d89e5006ab729657bd139">setClassifierModel</a> (<a class="code" href="structpcl_1_1_s_v_m_model.html">SVMModel</a> model)</div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;      {</div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;        <span class="comment">// model (inner pointers are references)</span></div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;        model_ = model;</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;        <span class="keywordtype">int</span> i = 0;</div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160; </div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;        <span class="keywordflow">while</span> (model_.scaling[i].index != -1)</div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;          i++;</div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160; </div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;        scaling_.max = i;</div>
<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;        scaling_.obj = Malloc (<span class="keyword">struct</span> <a class="code" href="structsvm__node.html">svm_node</a>, i + 1);</div>
<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;        scaling_.obj[i].index = -1;</div>
<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160; </div>
<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;        <span class="comment">// Performing full scaling copy</span></div>
<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; i; j++)</div>
<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;        {</div>
<div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;          scaling_.obj[j] = model_.scaling[j];</div>
<div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;        }</div>
<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160; </div>
<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;        model_extern_copied_ = 1;</div>
<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;      };</div>
<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160; </div>
<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;      <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00474"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a1c882ec948eca90fd357c008ec450e62">  474</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a1c882ec948eca90fd357c008ec450e62">loadClassProblem</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename)</div>
<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;      {</div>
<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;        assert (model_.l != 0);</div>
<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160; </div>
<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;        <span class="keywordtype">bool</span> out = <a class="code" href="classpcl_1_1_s_v_m.html#a0ec65c97223faf337970f834f304746f">SVM::loadProblem</a> (filename, prob_);</div>
<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;        <a class="code" href="classpcl_1_1_s_v_m.html#ad8e2c4441d6288270bdf7c36f4c4bbb2">SVM::adaptLibSVMToInput</a> (training_set_, prob_);</div>
<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;        <a class="code" href="classpcl_1_1_s_v_m_classify.html#ae4c3f1f28b86a9f743f23c049f37b75a">scaleProblem</a> (prob_, scaling_);</div>
<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;        <span class="keywordflow">return</span> out;</div>
<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;      };</div>
<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160; </div>
<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;      <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00488"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#ae81b26c1175ab555d1b4874314d4c61e">  488</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#ae81b26c1175ab555d1b4874314d4c61e">loadNormClassProblem</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename)</div>
<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;      {</div>
<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;        <span class="keywordtype">bool</span> out = <a class="code" href="classpcl_1_1_s_v_m.html#a0ec65c97223faf337970f834f304746f">SVM::loadProblem</a> (filename, prob_);</div>
<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;        <a class="code" href="classpcl_1_1_s_v_m.html#ad8e2c4441d6288270bdf7c36f4c4bbb2">SVM::adaptLibSVMToInput</a> (training_set_, prob_);</div>
<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;        <span class="keywordflow">return</span> out;</div>
<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;      };</div>
<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160; </div>
<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00498"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a49b336159e4a2e65510c6c814eed8a0e">  498</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a49b336159e4a2e65510c6c814eed8a0e">setProbabilityEstimates</a> (<span class="keywordtype">bool</span> set)</div>
<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;      {</div>
<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;        predict_probability_ = set;</div>
<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;      };</div>
<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160; </div>
<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;      <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00507"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a98351cb37844c007c274895ba067eaa0">  507</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a98351cb37844c007c274895ba067eaa0">classificationTest</a> ();</div>
<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160; </div>
<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;      <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00513"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a41a68f2bc37bb8d74a7fbc863a824ea8">  513</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a41a68f2bc37bb8d74a7fbc863a824ea8">classification</a> ();</div>
<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160; </div>
<div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;      std::vector&lt;double&gt;</div>
<div class="line"><a name="l00517"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a38348996b1cccff816a7947cbdb674da">  517</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a38348996b1cccff816a7947cbdb674da">classification</a> (<a class="code" href="structpcl_1_1_s_v_m_data.html">SVMData</a> in);</div>
<div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160; </div>
<div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;      <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00522"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a008cb8e748576935f45c4d77c177aefb">  522</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a008cb8e748576935f45c4d77c177aefb">saveClassProblem</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename)</div>
<div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;      {</div>
<div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_s_v_m.html#ac256c5efa13d6759dfc6e77e17eae4e6">SVM::saveProblem</a> (filename, 0);</div>
<div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;      };</div>
<div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160; </div>
<div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;      <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00530"></a><span class="lineno"><a class="line" href="classpcl_1_1_s_v_m_classify.html#a0473ccd43205fc95c9f1cf0e527575ea">  530</a></span>&#160;      <a class="code" href="classpcl_1_1_s_v_m_classify.html#a0473ccd43205fc95c9f1cf0e527575ea">saveNormClassProblem</a> (<span class="keyword">const</span> <span class="keywordtype">char</span> *filename)</div>
<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;      {</div>
<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1_s_v_m.html#ae604dbc0d535f8b0064b95cac1a15895">SVM::saveProblemNorm</a> (filename, prob_, 0);</div>
<div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;      };</div>
<div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;  };</div>
<div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;}</div>
<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160; </div>
<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;<span class="preprocessor">#endif </span><span class="comment">// PCL_SVM_WRAPPER_H_</span></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html">pcl::SVMClassify</a></div><div class="ttdoc">SVM (Support Vector Machines) classification of a dataset. It can be used both for testing a classifi...</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:365</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a008cb8e748576935f45c4d77c177aefb"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a008cb8e748576935f45c4d77c177aefb">pcl::SVMClassify::saveClassProblem</a></div><div class="ttdeci">bool saveClassProblem(const char *filename)</div><div class="ttdoc">Save the raw classification problem in a file (in svmlight format).</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:522</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a0473ccd43205fc95c9f1cf0e527575ea"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a0473ccd43205fc95c9f1cf0e527575ea">pcl::SVMClassify::saveNormClassProblem</a></div><div class="ttdeci">bool saveNormClassProblem(const char *filename)</div><div class="ttdoc">Save the normalized classification problem in a file (in svmlight format).</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:530</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a1c882ec948eca90fd357c008ec450e62"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a1c882ec948eca90fd357c008ec450e62">pcl::SVMClassify::loadClassProblem</a></div><div class="ttdeci">bool loadClassProblem(const char *filename)</div><div class="ttdoc">Read in a raw classification problem (in svmlight format). The values are normalized using the classi...</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:474</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a1f8c738896fa7d85def12eb947891bc4"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a1f8c738896fa7d85def12eb947891bc4">pcl::SVMClassify::resetTrainingSet</a></div><div class="ttdeci">void resetTrainingSet()</div><div class="ttdoc">Reset the training set.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:424</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a31aa9eab4c87ee424344e66b58f64e3b"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a31aa9eab4c87ee424344e66b58f64e3b">pcl::SVMClassify::~SVMClassify</a></div><div class="ttdeci">~SVMClassify()</div><div class="ttdoc">Destructor.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:392</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a31fc76d6055887a06e08d5e6dc23d5ec"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a31fc76d6055887a06e08d5e6dc23d5ec">pcl::SVMClassify::getInputTrainingSet</a></div><div class="ttdeci">std::vector&lt; SVMData &gt; getInputTrainingSet()</div><div class="ttdoc">Return the current training set.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:417</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a38348996b1cccff816a7947cbdb674da"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a38348996b1cccff816a7947cbdb674da">pcl::SVMClassify::classification</a></div><div class="ttdeci">std::vector&lt; double &gt; classification(SVMData in)</div><div class="ttdoc">Start the classification on a single set.</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a41a68f2bc37bb8d74a7fbc863a824ea8"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a41a68f2bc37bb8d74a7fbc863a824ea8">pcl::SVMClassify::classification</a></div><div class="ttdeci">bool classification()</div><div class="ttdoc">Start the classification on un-labelled input dataset. To get the classification result,...</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a42721792e7fbad28333c9cdbcdfb79b4"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a42721792e7fbad28333c9cdbcdfb79b4">pcl::SVMClassify::saveClassificationResult</a></div><div class="ttdeci">void saveClassificationResult(const char *filename)</div><div class="ttdoc">Save the classification result in an extern file.</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a49b336159e4a2e65510c6c814eed8a0e"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a49b336159e4a2e65510c6c814eed8a0e">pcl::SVMClassify::setProbabilityEstimates</a></div><div class="ttdeci">void setProbabilityEstimates(bool set)</div><div class="ttdoc">Set whether the classification has to be done with the probability estimate. (the classifier model ha...</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:498</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a8a44eed8244805fb26112aef65153b5d"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a8a44eed8244805fb26112aef65153b5d">pcl::SVMClassify::SVMClassify</a></div><div class="ttdeci">SVMClassify()</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:386</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a98351cb37844c007c274895ba067eaa0"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a98351cb37844c007c274895ba067eaa0">pcl::SVMClassify::classificationTest</a></div><div class="ttdeci">bool classificationTest()</div><div class="ttdoc">Start the classification on labelled input dataset. It returns the accuracy percentage....</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_a9e5bb9b2305d89e5006ab729657bd139"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#a9e5bb9b2305d89e5006ab729657bd139">pcl::SVMClassify::setClassifierModel</a></div><div class="ttdeci">void setClassifierModel(SVMModel model)</div><div class="ttdoc">Set the classifier model.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:448</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_aa2e72c9b20d5f0b997b2146da40c75ef"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#aa2e72c9b20d5f0b997b2146da40c75ef">pcl::SVMClassify::setInputTrainingSet</a></div><div class="ttdeci">void setInputTrainingSet(std::vector&lt; SVMData &gt; training_set)</div><div class="ttdoc">It adds/store the training set with labelled data.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:400</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_aafd5d50bf3c35d31db549b0bff7595c2"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#aafd5d50bf3c35d31db549b0bff7595c2">pcl::SVMClassify::loadClassifierModel</a></div><div class="ttdeci">bool loadClassifierModel(const char *filename)</div><div class="ttdoc">Read in a classifier model (in svmlight format).</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_ae4c3f1f28b86a9f743f23c049f37b75a"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#ae4c3f1f28b86a9f743f23c049f37b75a">pcl::SVMClassify::scaleProblem</a></div><div class="ttdeci">void scaleProblem(svm_problem &amp;input, svm_scaling scaling)</div><div class="ttdoc">It scales the input dataset using the model information.</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_ae81b26c1175ab555d1b4874314d4c61e"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#ae81b26c1175ab555d1b4874314d4c61e">pcl::SVMClassify::loadNormClassProblem</a></div><div class="ttdeci">bool loadNormClassProblem(const char *filename)</div><div class="ttdoc">Read in a normalized classification problem (in svmlight format). The data are kept whitout normalizi...</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:488</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_classify_html_afc0eb03a8962cb637ac99eaf1e2d1afc"><div class="ttname"><a href="classpcl_1_1_s_v_m_classify.html#afc0eb03a8962cb637ac99eaf1e2d1afc">pcl::SVMClassify::getClassificationResult</a></div><div class="ttdeci">void getClassificationResult(std::vector&lt; std::vector&lt; double &gt; &gt; &amp;out)</div><div class="ttdoc">Get the result of the classification.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:436</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html"><div class="ttname"><a href="classpcl_1_1_s_v_m.html">pcl::SVM</a></div><div class="ttdoc">Base class for SVM SVM (Support Vector Machines).</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:130</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_a0ec65c97223faf337970f834f304746f"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#a0ec65c97223faf337970f834f304746f">pcl::SVM::loadProblem</a></div><div class="ttdeci">bool loadProblem(const char *filename, svm_problem &amp;prob)</div><div class="ttdoc">Load a problem from an extern file.</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_a25c695eb4d94ac665b2ecccb4497aa85"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#a25c695eb4d94ac665b2ecccb4497aa85">pcl::SVM::readline</a></div><div class="ttdeci">char * readline(FILE *input)</div><div class="ttdoc">To read a line from the input file. Stored in &quot;line_&quot;.</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_a3f59b5f036691d050e7d1b8845083732"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#a3f59b5f036691d050e7d1b8845083732">pcl::SVM::saveClassifierModel</a></div><div class="ttdeci">void saveClassifierModel(const char *filename)</div><div class="ttdoc">Save the classifier model in an extern file (in svmlight format).</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:218</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_a42bf58b7fe08d473a2f4a595ded0fb4b"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#a42bf58b7fe08d473a2f4a595ded0fb4b">pcl::SVM::getClassName</a></div><div class="ttdeci">const std::string &amp; getClassName() const</div><div class="ttdoc">Get a string representation of the name of this class.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:159</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_aa1bb58c6d4ea4cc0bef4343371da4307"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#aa1bb58c6d4ea4cc0bef4343371da4307">pcl::SVM::~SVM</a></div><div class="ttdeci">~SVM()</div><div class="ttdoc">Destructor.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:188</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_ab9110ca99ee2fa34573133fe4a083fe2"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#ab9110ca99ee2fa34573133fe4a083fe2">pcl::SVM::adaptInputToLibSVM</a></div><div class="ttdeci">void adaptInputToLibSVM(std::vector&lt; SVMData &gt; training_set, svm_problem &amp;prob)</div><div class="ttdoc">Convert the input format (vector of SVMData) into a readable format for libSVM.</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_ac256c5efa13d6759dfc6e77e17eae4e6"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#ac256c5efa13d6759dfc6e77e17eae4e6">pcl::SVM::saveProblem</a></div><div class="ttdeci">bool saveProblem(const char *filename, bool labelled)</div><div class="ttdoc">Save the raw problem in an extern file.</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_acc4bad66d55915dd09977192ab8311b5"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#acc4bad66d55915dd09977192ab8311b5">pcl::SVM::getLabel</a></div><div class="ttdeci">void getLabel(std::vector&lt; int &gt; &amp;labels)</div><div class="ttdoc">Return the labels order from the classifier model.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:205</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_acf652ea11ea0c106b1cbbce00554185c"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#acf652ea11ea0c106b1cbbce00554185c">pcl::SVM::SVM</a></div><div class="ttdeci">SVM()</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:181</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_ad8e2c4441d6288270bdf7c36f4c4bbb2"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#ad8e2c4441d6288270bdf7c36f4c4bbb2">pcl::SVM::adaptLibSVMToInput</a></div><div class="ttdeci">void adaptLibSVMToInput(std::vector&lt; SVMData &gt; &amp;training_set, svm_problem prob)</div><div class="ttdoc">Convert the libSVM format (svm_problem) into a easier output format.</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_ae13d89577f55fcff9a570ccf77ee9e37"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#ae13d89577f55fcff9a570ccf77ee9e37">pcl::SVM::exitInputError</a></div><div class="ttdeci">void exitInputError(int line_num)</div><div class="ttdoc">Outputs an error in file reading.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:151</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_ae604dbc0d535f8b0064b95cac1a15895"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#ae604dbc0d535f8b0064b95cac1a15895">pcl::SVM::saveProblemNorm</a></div><div class="ttdeci">bool saveProblemNorm(const char *filename, svm_problem prob_, bool labelled)</div><div class="ttdoc">Save the problem (with normalized values) in an extern file.</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_html_af8b6779e2fbd5a636d79c5a8c77cf102"><div class="ttname"><a href="classpcl_1_1_s_v_m.html#af8b6779e2fbd5a636d79c5a8c77cf102">pcl::SVM::printNull</a></div><div class="ttdeci">static void printNull(const char *)</div><div class="ttdoc">Set for output printings during classification.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:144</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html">pcl::SVMTrain</a></div><div class="ttdoc">SVM (Support Vector Machines) training class for the SVM machine learning. It creates a model for the...</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:237</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_a187c6e44967d05ad37ecee814ff04a41"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#a187c6e44967d05ad37ecee814ff04a41">pcl::SVMTrain::doCrossValidation</a></div><div class="ttdeci">void doCrossValidation()</div><div class="ttdoc">To cross validate the classifier. It is automatic for probability estimate.</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_a3656dabad7ef92656ccfd319657ee392"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#a3656dabad7ef92656ccfd319657ee392">pcl::SVMTrain::resetTrainingSet</a></div><div class="ttdeci">void resetTrainingSet()</div><div class="ttdoc">Reset the training set.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:314</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_a4d3043d078eda40c1ced194ec6ef89df"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#a4d3043d078eda40c1ced194ec6ef89df">pcl::SVMTrain::~SVMTrain</a></div><div class="ttdeci">~SVMTrain()</div><div class="ttdoc">Destructor.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:271</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_a74a008298d1d42571b181c0383416010"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#a74a008298d1d42571b181c0383416010">pcl::SVMTrain::getClassifierModel</a></div><div class="ttdeci">SVMModel getClassifierModel()</div><div class="ttdoc">Return the result of the training.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:293</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_a83f2a5e13dd354bd0fdaab63eebc1eea"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#a83f2a5e13dd354bd0fdaab63eebc1eea">pcl::SVMTrain::setInputTrainingSet</a></div><div class="ttdeci">void setInputTrainingSet(std::vector&lt; SVMData &gt; training_set)</div><div class="ttdoc">It adds/store the training set with labelled data.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:300</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_a910cc9a619fd9f4c28e1af99f28aa002"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#a910cc9a619fd9f4c28e1af99f28aa002">pcl::SVMTrain::loadProblem</a></div><div class="ttdeci">bool loadProblem(const char *filename)</div><div class="ttdoc">Read in a problem (in svmlight format).</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:327</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_a9937510055076d410ebb80def036e020"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#a9937510055076d410ebb80def036e020">pcl::SVMTrain::getInputTrainingSet</a></div><div class="ttdeci">std::vector&lt; SVMData &gt; getInputTrainingSet()</div><div class="ttdoc">Return the current training set.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:307</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_a993cd35745435d46b54597846a03005a"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#a993cd35745435d46b54597846a03005a">pcl::SVMTrain::saveNormTrainingSet</a></div><div class="ttdeci">bool saveNormTrainingSet(const char *filename)</div><div class="ttdoc">Save the normalized training set in a file (in svmlight format).</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:355</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_a9fc90b124c8c72e8c82e9d0657a8d22e"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#a9fc90b124c8c72e8c82e9d0657a8d22e">pcl::SVMTrain::saveTrainingSet</a></div><div class="ttdeci">bool saveTrainingSet(const char *filename)</div><div class="ttdoc">Save the raw training set in a file (in svmlight format).</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:347</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_ab860aa193b6775d19bc97ec3d8bedf8f"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#ab860aa193b6775d19bc97ec3d8bedf8f">pcl::SVMTrain::trainClassifier</a></div><div class="ttdeci">bool trainClassifier()</div><div class="ttdoc">Start the training of the SVM classifier.</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_ab9959f5e2ee6c39ac14ed8e09b6afc98"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#ab9959f5e2ee6c39ac14ed8e09b6afc98">pcl::SVMTrain::setDebugMode</a></div><div class="ttdeci">void setDebugMode(bool in)</div><div class="ttdoc">Set to 1 for debugging info.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:334</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_ab996f6bdd3ac6565558722f493c75e67"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#ab996f6bdd3ac6565558722f493c75e67">pcl::SVMTrain::scaleFactors</a></div><div class="ttdeci">void scaleFactors(std::vector&lt; SVMData &gt; training_set, svm_scaling &amp;scaling)</div><div class="ttdoc">It extracts scaling factors from the input training_set. The scaling of the training_set is a mandato...</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_ac032e2dc6d89909d72aebc5cb46806f8"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#ac032e2dc6d89909d72aebc5cb46806f8">pcl::SVMTrain::setParameters</a></div><div class="ttdeci">void setParameters(SVMParam param)</div><div class="ttdoc">Change default training parameters (pcl::SVMParam).</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:279</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_af70b307e8b0d305325380e49349c9158"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#af70b307e8b0d305325380e49349c9158">pcl::SVMTrain::getParameters</a></div><div class="ttdeci">SVMParam getParameters()</div><div class="ttdoc">Return the current training parameters.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:286</div></div>
<div class="ttc" id="aclasspcl_1_1_s_v_m_train_html_af958b338c84171fbc044b271561a66ef"><div class="ttname"><a href="classpcl_1_1_s_v_m_train.html#af958b338c84171fbc044b271561a66ef">pcl::SVMTrain::SVMTrain</a></div><div class="ttdeci">SVMTrain()</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:264</div></div>
<div class="ttc" id="astructpcl_1_1_s_v_m_data_html"><div class="ttname"><a href="structpcl_1_1_s_v_m_data.html">pcl::SVMData</a></div><div class="ttdoc">The structure stores the features and the label of a single sample which has to be used for the train...</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:118</div></div>
<div class="ttc" id="astructpcl_1_1_s_v_m_data_point_html"><div class="ttname"><a href="structpcl_1_1_s_v_m_data_point.html">pcl::SVMDataPoint</a></div><div class="ttdoc">The structure initialize a single feature value for the classification using SVM (Support Vector Mach...</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:105</div></div>
<div class="ttc" id="astructpcl_1_1_s_v_m_model_html"><div class="ttname"><a href="structpcl_1_1_s_v_m_model.html">pcl::SVMModel</a></div><div class="ttdoc">The structure initialize a model crated by the SVM (Support Vector Machines) classifier (pcl::SVMTrai...</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:93</div></div>
<div class="ttc" id="astructpcl_1_1_s_v_m_param_html"><div class="ttname"><a href="structpcl_1_1_s_v_m_param.html">pcl::SVMParam</a></div><div class="ttdoc">The structure stores the parameters for the classificationa nd must be initialized and passed to the ...</div><div class="ttdef"><b>Definition:</b> svm_wrapper.h:67</div></div>
<div class="ttc" id="astructsvm__model_html"><div class="ttname"><a href="structsvm__model.html">svm_model</a></div><div class="ttdef"><b>Definition:</b> svm.h:109</div></div>
<div class="ttc" id="astructsvm__node_html"><div class="ttname"><a href="structsvm__node.html">svm_node</a></div><div class="ttdef"><b>Definition:</b> svm.h:53</div></div>
<div class="ttc" id="astructsvm__parameter_html"><div class="ttname"><a href="structsvm__parameter.html">svm_parameter</a></div><div class="ttdef"><b>Definition:</b> svm.h:84</div></div>
<div class="ttc" id="astructsvm__problem_html"><div class="ttname"><a href="structsvm__problem.html">svm_problem</a></div><div class="ttdef"><b>Definition:</b> svm.h:59</div></div>
<div class="ttc" id="astructsvm__scaling_html"><div class="ttname"><a href="structsvm__scaling.html">svm_scaling</a></div><div class="ttdef"><b>Definition:</b> svm.h:67</div></div>
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